Estimating phenotype networks is a growing field in computational biology. It helps deepen the understanding of disease etiology and is useful in many applications. In this study, we present a method that constructs a phenotype network by assuming a Gaussian linear structure model embedding a directed acyclic graph (DAG). We utilize genetic variants as instrumental variables and show how our method only requires access to summary statistics from a genome-wide association study (GWAS) and a reference panel of genotype data. Besides estimation, a distinct feature of the method is its summary statistics-based likelihood ratio test on directed edges. We applied our method to estimate a causal network of 29 cardiovascular-related proteins and linked the estimated network to Alzheimer's disease (AD). A simulation study was conducted to demonstrate the effectiveness of this method. An R package implementing the proposed method and an R Shiny App for the visualization of the estimated protein network are being made available.
Data collected in clinical trials are often composed of multiple types of variables. For example, laboratory measurements and vital signs are longitudinal data of continuous or categorical variables, adverse events may be recurrent events, and death is a time-to-event variable. Missing data due to patients' discontinuation from the study or as a result of handling intercurrent events using a hypothetical strategy almost always occur during any clinical trial. Imputing these data with mixed types of variables simultaneously is a challenge that has not been studied. In this article, we propose using an approximate fully conditional specification to impute the missing data. Simulation shows the proposed method provides satisfactory results under the assumption of missing at random. Finally, real data from a major diabetes clinical trial are analyzed to illustrate the potential benefit of the proposed method.
Ultra-high field (7T) ultra-short echo time (8 ms STEAM) MRS can consistently provide a 14-neurochemical profile and distinguish aging and AD status. In this project, we generated composite scores from published age- and AD- specific neurochemical profiles and applied them to a typically aging cohort for whom polygenic risk for AD and extensive cognitive performance data were also available. Principal component analysis of the neurochemical profile fully distinguished aging, and AD classification was 58% correct. The largest correlation coefficients (r) were found between MRS and cognition. Correlation between MRS and polygenic risk and between cognition and polygenic risk was smaller.
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